class_docstr: CategoricalCNNPolicy.
  
  A policy that contains a CNN and a MLP to make prediction based on
  a categorical distribution.
  
  It only works with akro.Discrete action space.
  
  Args:
      env_spec (garage.envs.env_spec.EnvSpec): Environment specification.
      filters (Tuple[Tuple[int, Tuple[int, int]], ...]): Number and dimension
          of filters. For example, ((3, (3, 5)), (32, (3, 3))) means there
          are two convolutional layers. The filter for the first layer have 3
          channels and its shape is (3 x 5), while the filter for the second
          layer have 32 channels and its shape is (3 x 3).
      strides (tuple[int]): The stride of the sliding window. For
          example, (1, 2) means there are two convolutional layers. The
          stride of the filter for first layer is 1 and that of the second
          layer is 2.
      padding (str): The type of padding algorithm to use,
          either 'SAME' or 'VALID'.
      name (str): Policy name, also the variable scope of the policy.
      hidden_sizes (list[int]): Output dimension of dense layer(s).
          For example, (32, 32) means the MLP of this policy consists
          of two hidden layers, each with 32 hidden units.
      hidden_nonlinearity (callable): Activation function for intermediate
          dense layer(s). It should return a tf.Tensor. Set it to
          None to maintain a linear activation.
      hidden_w_init (callable): Initializer function for the weight
          of intermediate dense layer(s). The function should return a
          tf.Tensor.
      hidden_b_init (callable): Initializer function for the bias
          of intermediate dense layer(s). The function should return a
          tf.Tensor.
      output_nonlinearity (callable): Activation function for output dense
          layer. It should return a tf.Tensor. Set it to None to
          maintain a linear activation.
      output_w_init (callable): Initializer function for the weight
          of output dense layer(s). The function should return a
          tf.Tensor.
      output_b_init (callable): Initializer function for the bias
          of output dense layer(s). The function should return a
          tf.Tensor.
      layer_normalization (bool): Bool for using layer normalization or not.
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docstr: Initialize policy.
docstr_tok: ["Initialize","policy","."]
code: def _initialize(self):
      """Initialize policy."""
      flat_dim = np.prod(self._obs_dim)
      state_input = tf.compat.v1.placeholder(
          tf.float32, shape=(None, None, flat_dim)
      )
      if isinstance(self.env_spec.observation_space, akro.Image):
          augmented_state_input = tf.cast(state_input, tf.float32)
          augmented_state_input /= 255.0
      else:
          augmented_state_input = state_input
      dist = self.build(augmented_state_input).outputs
      self._f_prob = tf.compat.v1.get_default_session().make_callable(
          [
              tf.argmax(
                  dist.sample(seed=deterministic.get_tf_seed_stream()),
                  -1,
              ),
              dist.probs,
          ],
          feed_list=[state_input],
      )
  
file_path: garage/src/garage/tf/policies/categorical_cnn_policy.py
imports: import akro
  import numpy
  import tensorflow
  from garage.experiment import deterministic
  from garage.tf.models import CategoricalCNNModel
  from garage.tf.policies.policy import Policy
label_desc: Use image node_id 2 for calling the CategoricalCNNPolicy obj's underlying member method code with example usage: obj._initialize() without return types